Though commonly studied together in the domains of statistics [1]}, meteorological forecast [2]}, medical analysis [3]}; for recent approaches proposed in the deep learning domain, the joint importance has been sidelined for individual improvements. Many of the recently proposed methods employ strictly proper scores such as Brier Score [4]} (mean squared error) and negative log-likelihood to measure calibration. Such scores have been known to decompose into calibration, and refinement components [5]}. However, a metric which produces a single score reflecting 2 complex attributes can conceal the area in which the improvement is made. In this regard, many approaches utilise Expected Calibration Error (ECE) [6]}, [7]} to focus only on the calibration aspect of a forecaster. Motivated from reliability diagrams, it measures the difference between model confidence and accuracy computed over various bins.
